EP4607404B1 - Lecture d'un code optique - Google Patents

Lecture d'un code optique

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Publication number
EP4607404B1
EP4607404B1 EP24158903.5A EP24158903A EP4607404B1 EP 4607404 B1 EP4607404 B1 EP 4607404B1 EP 24158903 A EP24158903 A EP 24158903A EP 4607404 B1 EP4607404 B1 EP 4607404B1
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EP
European Patent Office
Prior art keywords
code
criterion
accordance
binarization
image data
Prior art date
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Active
Application number
EP24158903.5A
Other languages
German (de)
English (en)
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EP4607404A1 (fr
Inventor
Jonathan Steinbuch
Vicky Hallmann
Sofia Bär
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Sick AG
Original Assignee
Sick AG
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Priority to EP24158903.5A priority Critical patent/EP4607404B1/fr
Priority to US19/038,592 priority patent/US20250265432A1/en
Publication of EP4607404A1 publication Critical patent/EP4607404A1/fr
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Publication of EP4607404B1 publication Critical patent/EP4607404B1/fr
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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
    • G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
    • G06K7/14—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
    • G06K7/1404—Methods for optical code recognition
    • G06K7/146—Methods for optical code recognition the method including quality enhancement steps
    • G06K7/1473—Methods for optical code recognition the method including quality enhancement steps error correction
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
    • G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
    • G06K7/14—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
    • G06K7/1404—Methods for optical code recognition
    • G06K7/1408—Methods for optical code recognition the method being specifically adapted for the type of code
    • G06K7/1417—2D bar codes
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
    • G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
    • G06K7/14—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
    • G06K7/1404—Methods for optical code recognition
    • G06K7/1439—Methods for optical code recognition including a method step for retrieval of the optical code
    • G06K7/1447—Methods for optical code recognition including a method step for retrieval of the optical code extracting optical codes from image or text carrying said optical code
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
    • G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
    • G06K7/14—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
    • G06K7/1404—Methods for optical code recognition
    • G06K7/1439—Methods for optical code recognition including a method step for retrieval of the optical code
    • G06K7/1452—Methods for optical code recognition including a method step for retrieval of the optical code detecting bar code edges
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
    • G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
    • G06K7/14—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
    • G06K7/1404—Methods for optical code recognition
    • G06K7/146—Methods for optical code recognition the method including quality enhancement steps
    • G06K7/1465—Methods for optical code recognition the method including quality enhancement steps using several successive scans of the optical code
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
    • G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
    • G06K7/14—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
    • G06K7/1404—Methods for optical code recognition
    • G06K7/146—Methods for optical code recognition the method including quality enhancement steps
    • G06K7/1491—Methods for optical code recognition the method including quality enhancement steps the method including a reconstruction step, e.g. stitching two pieces of bar code together to derive the full bar code

Definitions

  • the invention relates to a method for reading an optical code according to the preamble of claim 1 and an optoelectronic code reader according to claim 12.
  • Code readers are commonly found at supermarket checkouts, for automatic package identification, mail sorting, baggage handling at airports, and in other logistics applications.
  • a reading beam is guided across the code using a rotating mirror or a polygonal mirror wheel.
  • a camera-based code reader uses an image sensor to capture images of the objects with the codes on them, and image analysis software extracts the code information from these images.
  • the code-bearing objects are conveyed past the code reader.
  • a scanning code reader captures the codes as they are successively guided into its reading area.
  • a line scan camera reads the object images containing the code information successively and line by line, capturing the relative movement.
  • a two-dimensional image sensor regularly records image data, which overlaps to a greater or lesser extent depending on the recording frequency and conveying speed.
  • several code readers are often installed on a single reading tunnel to capture objects from multiple or all sides.
  • a scanning code reader also captures the return of the object and thus ultimately image lines that can be combined to form an object image, although an image sensor is preferred for this purpose in practice. Code areas can be identified in such an object image, and one- or two-dimensional codes can be read.
  • a high read rate is one of the most important quality criteria. Reading errors necessitate costly corrective actions, such as manual rescanning or re-sorting. In practice, however, defects frequently occur within the code area, which are collectively referred to here as faults or defects that hinder or prevent reading. There are various causes for faults or defects, such as mechanical stress on the code due to damage, poor printing, or contamination; obstruction of code parts; or optical impairments such as reflections, blurring, or low contrast.
  • the method is a computer-implemented method that runs, for example, in a processing unit of a code reader or other sensor acquiring image data and/or an attached processing unit.
  • the optical code preferably one of the codes mentioned in the introduction with Reed-Solomon correction words and, in particular, a 2D code, comprises a first plurality of codewords.
  • a codeword is composed of a second plurality of code modules, for example, eight code modules, each of which individually encodes one bit via light and dark and together encodes one byte or eight bits of the codeword.
  • the codewords are translated into respective characters of the code content conveyed by the optical code.
  • image data containing the optical code is first acquired using one of the known methods described in the introduction. Strictly speaking, this is only a code candidate, as whether it is a readable, complete code will only become clear later. This distinction is not made here.
  • the image data is preferably pre-processed to target a specific area of the code.
  • the module size can now be defined: this is the extent of a code module in pixels of the image data, which are naturally specified in other units and can be estimated using various known methods.
  • defects are identified, whereby one defect has already been introduced at the beginning, and in the area of the image data affected by the defect, at least one code module is not recognizable or not reliably recognizable.
  • the image data is then evaluated, preferably as explained in more detail below, taking the defects into account, in order to read the codewords.
  • the binarization criterion checks whether a code module has been or will be incorrectly classified during a previous or upcoming binarization, i.e., whether it appears light in the binarized image data but dark in reality, or vice versa.
  • the probability of such a binarization error increases the closer the gray values of the code module are to a binarization threshold.
  • the area checked for this purpose is preferably approximately the size of a code module, since the gray values of the code module being checked are the primary concern. However, the contrast to neighboring code modules also plays a role, and therefore the area can be somewhat larger than a code module.
  • the binarization criterion checks gray-valued image data for defects; that is, brightness values are quantified for each pixel, for example, in the interval [0...255]. In this sense, red, blue, green, or other colored image data can also be considered gray-valued.
  • the edge criterion can also be applied to gray values or, alternatively, to binarized values.
  • the invention has the advantage that defects in the optical code to be read are reliably detected.
  • the applied criteria do not depend on specific code features, such as finder patterns or an outer code contour.
  • the edge criterion requires at least largely correct detection of existing contours.
  • the binarization criterion assesses whether this requirement can be assumed, i.e., the reliability of contour detection, so that the two criteria complement each other very well. Since it is thus known where defects are located in the optical code, in preferred embodiments with subsequent error correction, it is possible to mark which code modules or codewords are affected by defects.
  • the error correction draws on knowledge of the number and position of the defects.
  • the error correction system's capacity is significantly improved. Effectively, twice as many errors can be corrected compared to when the error correction system had to locate the errors itself.
  • edge detection is preferably performed in the image data, especially after artificially creating a blur beforehand. This makes the edges to be evaluated by the edge criterion, or the absence of edges in certain areas, more easily accessible.
  • the image data is preferably blurred beforehand, for example with a Gaussian kernel whose kernel size is based on the module size.
  • edge detection is preferably performed on grayscale values, but it is also possible to use the module transitions as edges in binarized image data.
  • One method that can be used for this is the Canny algorithm.
  • the distance to the nearest edge is determined.
  • the edge criterion is considered fulfilled at the respective pixel if the distance corresponds to at least a predefined value.
  • the distance map is calculated, for example, using a distance transformation according to Rosenfeld and Pfalz.
  • the distance map clearly shows the size of an edge-free surrounding area of each pixel, so that defects according to the edge criterion can be detected by a threshold operation with a predefined distance corresponding to the predefined size of an edge-free area considered a defect.
  • the distance to the nearest edge can be calculated for all pixels of the acquired image data, or only one pixel or a few representative pixels of the respective code modules can be used.
  • the grid of the code modules is determined by the module size.
  • the binarization criterion is preferably considered fulfilled at a pixel if the pixel's gray value remains within an expected range of variation around the binarization threshold, particularly within a fraction of a standard deviation of the gray values of pixels in a neighborhood of the pixel.
  • the binarization threshold is typically estimated from a brightness distribution of the image data, globally from all gray values, or preferably locally for a given neighborhood. In this context, a corresponding global or local range of variation, in particular variance or standard deviation, can also be determined. This range of variation is a suitable measure for assessing how close the gray values of a code module are to the binarization threshold.
  • the binarization criterion considers the binarization of this code module to be unreliable, since even small errors would tip the binarization in the other direction towards light or dark, and this in turn is considered an indication of a defect.
  • a confidence value is used to determine which codewords affected by errors are submitted to the correction process.
  • the errors, or rather the codewords affected by them, are thus prioritized based on this confidence value. For example, a minimum threshold is applied to the confidence value, and from the remaining errors, an r max selection is made. These are primarily the r max errors with the highest confidence values. It is conceivable to attempt decoding with different r max selections sequentially, as long as a successful read is not achieved and decoding time remains available, which is limited, especially in real-time applications. If there are more than r max errors with high confidence, this can also be the basis for deciding that the code is fundamentally unreadable.
  • the confidence score is preferably calculated from the edge criterion and/or the binarization criterion.
  • the two criteria can be used individually or together to assess whether a codeword should be considered erroneous due to a defect.
  • the edge criterion preferably bears the following according to the formula 1 ⁇ 1 2 n 1 This formula contributes to the confidence value, where n ⁇ sub> 1 ⁇ /sub> is the number of code modules affected by an error. It compares this to the likelihood that n ⁇ sub>1 ⁇ /sub> identical code modules will occur sequentially as part of a regular code and not caused by errors.
  • the binarization criterion contributes more to the confidence value the closer the gray value of the pixel considered with the binarization criterion is to the
  • the confidence value quantifies the distance to the binarization threshold, whether linear or with a different weighting. Therefore, the closer a code module's gray values are to the binarization threshold, the more likely it is to be incorrectly binarized and thus considered a fault.
  • an optoelectronic code reader preferably a camera-based code reader
  • at least one light receiving element for generating image data from received light
  • an internal and/or external control and evaluation unit in which a method according to the invention for reading optical codes is implemented.
  • Image acquisition is carried out, as described in the introduction, with an image sensor with matrix-arranged pixels, with a line sensor in relative motion to the optical code, or by scanning.
  • Figure 1 shows an optoelectronic code reader 10 mounted above a conveyor belt 12, which conveys objects 14, as indicated by arrow 16, through the detection area 18 of the code reader 10.
  • the objects 14 bear optical codes 20 on their outer surfaces, which are detected and evaluated by the code reader 10.
  • the optical codes 20 are preferably codes with a Reed-Solomon encoding, as already mentioned by way of example in the introduction, and they are applied in any manner, in particular printed directly onto an object 14 or applied via a label.
  • the optical codes 20 can only be recognized by the code reader 10 if they are attached to the top surface or at least visible from above. Therefore, contrary to the illustration in Figure 1
  • a plurality of code readers 10 can be mounted from different directions to enable so-called omnidirectional reading from all directions.
  • the arrangement of the multiple code readers 10 into a reading system is usually implemented as a reading tunnel.
  • This stationary application of the code reader 10 on a conveyor belt is very common in practice.
  • the invention relates to the reading of codes or the code reader 10 itself, so this example should not be understood as limiting.
  • codes can also be scanned manually, or in a presentation application, a code or an object 14 with a code can be held in the reading field of the code reader 10.
  • the code reader 10 uses a light receiver 24 to capture image data of the conveyed objects 14 and the optical codes 20, which are then further processed by a control and evaluation unit 26 using image evaluation and decoding methods.
  • the control and evaluation unit 26 comprises, for example, at least one computing component such as a microprocessor or CPU (Central Processing Unit), an FPGA (Field Programmable Gate Array), a DSP (Digital Signal Processor), an ASIC (Application-Specific Integrated Circuit), a K-processor, an NPU (Neural Processing Unit), a GPU (Graphics Processing Unit), a VPU (Video Processing Unit), or the like.
  • the specific imaging method is not essential for the invention, so the code reader 10 can be constructed according to any known principle.
  • Figure 2 Figure 1 shows an exemplary optical code 20 with a defect 30.
  • the defect is an erasure.
  • the actual cause of the defect 30 is not relevant to the invention; for example, the defect 30 could be due to the optical code 20 itself, such as a misprint, contamination, obstruction, or reflection, the recording situation, or the code reader 10.
  • the defect 30 is shown primarily for illustrative purposes and in this extent; according to the invention, multiple, even smaller, defects 30 are also detected. It should therefore be understood as an example, and it is also irrelevant whether the optical code 20 is still readable with such an extensive defect 30. On the contrary, being able to detect this can be an advantage of the method according to the invention.
  • Figure 3 shows an exemplary flowchart for detecting defects 30 and reading optical codes 20, taking the defects 30 into account.
  • the entire code is referred to as the optical code 20, which, in the context of code reading, is also called a symbol.
  • a codeword is a single character of the optical code 20, which, for example, consists of eight code modules or bits. It should be noted that this terminology differs from that of coding theory, in which Reed-Solomon error correction is actually based. In coding theory, a codeword would be called a symbol.
  • step S1 an image with an optical code 20, or a potential optical code or code candidate, is captured.
  • a rectangular, axis-oriented section is obtained that covers the optical code 20 and on which further recognition can be limited.
  • steps S2 and S3 the flowchart splits into two paths that can be processed sequentially or in parallel.
  • Faults 30 are therefore detected using an edge criterion and/or a binarization criterion.
  • the edge criterion identifies larger edgeless areas, which particularly indicate the absence of code modules due to erasure, while the binarization criterion is related to low contrast and identifies areas where the distinction between light and dark is unreliable, thus increasing the probability of incorrect binarization.
  • edge detection is performed for evaluation using the edge criterion.
  • artificial blurring is introduced beforehand to wash out noise and retain only significant edges.
  • a Gaussian filter for example, is suitable for this purpose; its width is chosen depending on the module size and the extent of the noise, so that only noise artifacts and not code modules are blurred.
  • One possible edge detector that proceeds in this way is the Canny algorithm, although other well-known edge detectors can also be used.
  • Figure 4 shows a representation of an edge image corresponding to the optical code according to Figure 2 .
  • edges can be detected in a grayscale image, or the module transitions in a binarized image can be used as edges.
  • edges can be restricted to module sampling points. In this case, not all pixels are considered, but only one or a few representative pixels per code module.
  • the grid of such module sampling points, corresponding to the code modules, can be derived from the module size.
  • step S5 the distance to the nearest edge is calculated for each pixel in the edge image.
  • This can again be applied to all pixels or only to module sampling points.
  • the distance transformation according to Rosenfeld and Pfalz is used, although other methods are also possible.
  • Figure 5 shows a representation of an edge image according to Figure 4
  • the applied distance transformation where brighter pixels represent larger distances, is used.
  • the brighter a pixel is, the larger the edgeless area in which it lies. Therefore, a threshold, preferably dependent on the module size, can be set to detect defects 30 according to the edge criterion.
  • the threshold should be high enough to distinguish defects 30 from regular clusters of similar code modules. to distinguish.
  • Figure 6 shows an illustration of the optical code 20 according to Figure 2
  • a confidence value is preferably determined that assesses the reliability of the finding that it is indeed a defect 30, in the sense that a codeword is not reliably readable due to a defect 30. This is done by comparing, in particular, the probability that similar code modules are regularly clustered together, as explained below.
  • the binarization criterion is used to assess whether the grayscale values of a code module are close to a binarization threshold, as this indicates an increased probability that the code module is incorrectly binarized.
  • the binarization threshold is the limit that determines whether a pixel is classified as light or dark during binarization or when generating a black-and-white image from a grayscale image.
  • the binarization threshold can be derived, for example, from a brightness or grayscale distribution.
  • a range or standard deviation can also be specified for the binarization threshold.
  • a global binarization threshold is not determined, although this would be possible. Instead, a local binarization threshold is determined for a neighborhood of a considered code module; the same applies to the range.
  • the range for example, x standard deviations with x ⁇ 1, x ⁇ 0.5, or similar, can then be used to measure whether the grayscale values of a code module differ significantly from the binarization threshold.
  • the confidence value can also be specified as the distance to the binarization threshold.
  • step S7 the detected defects 30, initially located in a code module, are assigned a codeword. This assignment is determined by the respective code standard and is therefore not described in detail. Thus, at this point, it is known which codewords in how many code modules are affected by defects 30, as well as which criterion, and with what confidence level, detected the defect 30 or a codeword affected by a defect.
  • step S8 the confidence values are used to find codewords to be corrected.
  • r max preferably corresponds to the maximum number of code modules affected by a defect 30 and with which a subsequent error correction methods, or in particular the Reed-Solomon method, are still applicable. What this means will be explained in more detail later. First, however, we will discuss prioritization and confidence levels. For the edge criterion, a confidence level of 50% per code module can be assumed, which corresponds to the basic probability that each code module is either light or dark.
  • the linearly or non-linearly rescaled distance to the binarization threshold can indicate the confidence for a single code module that a binarization error has occurred, with the probability increasing the closer the code module is to the binarization threshold.
  • the confidences of its code modules are multiplied together. If both criteria are triggered for a codeword, the confidences are combined.
  • a minimum confidence level can now be specified to obtain codewords that are affected by a defect 30 with sufficient reliability. From this, a selection of at most r max codewords can then be made, preferably those with the highest confidence, but other selections, including random selection, are also conceivable. In particular, different selections can be made for repeated reading attempts.
  • the parameter r ⁇ sub> max ⁇ /sub> is derived from the error correction capacity.
  • An ( n, k ) Reed-Solomon code of message length n and data length k ⁇ n accordingly has n - k error correction words. If r errors (or codewords affected by an error) are marked within this code, the decoding reliability corresponds to that of an ( n - r, k ) Reed-Solomon code. Therefore, when r n - k, there is no further error checking.
  • step S9 an attempt is made to read the optical code 20 using an error correction procedure. Knowing the defects 30 and the codewords affected by them, the error correction procedure can correct twice as many errors, since no correction words are consumed for locating errors. Ideally, the reading attempt is successful in step S10, and the optical code is read. Code 20 is read. If the read attempt fails, further read attempts can be made, provided the application still has decoding time available. In particular, a different selection of faults 30 or affected codewords can be made in step S8. If the decoding time is exhausted or there are no further reasonable options for a read attempt, the optical code 20 remains unreadable in step S11, and a corresponding read error can be displayed.

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Claims (12)

  1. Procédé de lecture d'un code optique (20) comportant une première pluralité de mots de code provenant chacun d'une deuxième pluralité de modules de code, comprenant les étapes consistant à
    enregistrer des données d'image avec le code optique (20), les modules de code dans les données d'image ayant une taille de module qui indique la taille d'un module de code en points d'image des données d'image, déterminer des zones perturbées (30), une zone perturbée (30) étant une zone dans les données d'image dans laquelle au moins un module de code n'est pas reconnaissable, et
    évaluer les données d'image avec lecture des mots de code,
    caractérisé en ce que
    les zones perturbées (30) sont déterminées à l'aide d'un critère de bords et/ou d'un critère de binarisation,
    sachant que
    le critère de bords évalue si un module de code se trouve dans une zone des données d'image exempte de bords qui est supérieure à un multiple prédéfini de la taille du module, ce pourquoi une détection de bords est effectuée dans les données d'image, la distance par rapport au bord le plus proche est déterminée pour au moins un point d'image par position d'un module de code, et le critère de bords est considéré comme état satisfait dans le point d'image respectif si la distance correspond au moins à une distance prédéfinie,
    et le critère de binarisation évalue si un module de code avec des valeurs de gris proches d'un seuil de binarisation est enregistré, le critère de binarisation étant considéré comme étant satisfait au niveau d'un point d'image si la valeur de gris du point d'image reste dans une plage de variation attendue autour du seuil de binarisation.
  2. Procédé selon la revendication 1,
    dans lequel un flou est généré artificiellement avant la détection des bords.
  3. Procédé selon la revendication 1 ou 2,
    dans lequel le critère de binarisation est considéré comme étant satisfait au niveau d'un point d'image si la valeur de gris du point d'image reste au sein d'une fraction d'un écart type des valeurs de gris des points d'image au voisinage du point d'image.
  4. Procédé selon l'une des revendications précédentes,
    dans lequel le code optique (20) est lu à l'aide d'un procédé de correction d'erreurs.
  5. Procédé selon la revendication 4,
    dans lequel les zones perturbées (30) sont communiquées au procédé de correction d'erreurs en tant que valeurs d'entrée supplémentaires.
  6. Procédé selon la revendication 5,
    dans lequel un nombre de mots de code affectés par des zones perturbées (30) est communiqué au procédé de correction d'erreurs en fonction d'une capacité de correction d'erreurs du procédé de correction d'erreurs.
  7. Procédé selon la revendication 5 ou 6,
    dans lequel plusieurs tentatives de lecture sont effectuées avec le procédé de correction d'erreurs, et différents mots de code affectés par des zones perturbées (30) sont communiqués au procédé de correction d'erreurs lors des tentatives de lecture.
  8. Procédé selon l'une des revendications 5 à 7,
    dans lequel une valeur de confiance permet de décider quels mots de code affectés par des zones perturbées (30) sont communiqués au procédé de correction.
  9. Procédé selon la revendication 8,
    dans lequel la valeur de confiance est calculée à partir du critère de bords et/ou du critère de binarisation.
  10. Procédé selon la revendication 9,
    dans lequel le critère de bords contribue à la valeur de confiance selon la formule 1 − 1 2 n 1 , n 1 étant le nombre de modules de code affectés par une zone perturbée (30).
  11. Procédé selon la revendication 9 ou 10,
    dans lequel le critère de binarisation contribue d'autant plus à la valeur de confiance que la valeur de gris du point d'image considéré avec le critère de binarisation est proche du seuil de binarisation.
  12. Lecteur de code optoélectronique (10) comprenant au moins un élément de réception de lumière (24) pour générer des données d'image à partir de la lumière reçue et comprenant une unité de commande et d'évaluation (26) dans laquelle est mis en œuvre un procédé de lecture de codes optiques (20) selon l'une des revendications précédentes.
EP24158903.5A 2024-02-21 2024-02-21 Lecture d'un code optique Active EP4607404B1 (fr)

Priority Applications (2)

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